# Copyright 2020 Huawei Technologies Co., Ltd## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# ============================================================================# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Functions for computing metrics like precision, recall, CorLoc and etc."""

import numpy as np


def compute_precision_recall(scores, labels, num_gt):
    """Compute precision and recall.

    Args:
        scores: A float numpy array representing detection score
        labels: A boolean numpy array representing true/false positive labels
        num_gt: Number of ground truth instances

    Raises:
        ValueError: if the input is not of the correct format

    Returns:
        precision: Fraction of positive instances over detected ones. This
            value is None if no ground truth labels are present.
        recall: Fraction of detected positive instance over all positive
            instances. This value is None if no ground truth labels are
            present.
    """
    if (not isinstance(labels, np.ndarray) or labels.dtype != np.bool
            or len(labels.shape) != 1):
        raise ValueError('labels must be single dimension bool numpy array')

    if not isinstance(scores, np.ndarray) or len(scores.shape) != 1:
        raise ValueError('scores must be single dimension numpy array')

    if num_gt < np.sum(labels):
        raise ValueError(
            'Number of true positives must be smaller than num_gt.')

    if len(scores) != len(labels):
        raise ValueError('scores and labels must be of the same size.')

    if num_gt == 0:
        return None, None

    sorted_indices = np.argsort(scores)
    sorted_indices = sorted_indices[::-1]
    labels = labels.astype(int)
    true_positive_labels = labels[sorted_indices]
    false_positive_labels = 1 - true_positive_labels
    cum_true_positives = np.cumsum(true_positive_labels)
    cum_false_positives = np.cumsum(false_positive_labels)
    precision = cum_true_positives.astype(float) / (
        cum_true_positives + cum_false_positives)
    recall = cum_true_positives.astype(float) / num_gt
    return precision, recall


def compute_average_precision(precision, recall):
    """Compute Average Precision according to the definition in VOCdevkit.

    Precision is modified to ensure that it does not decrease as recall
    decrease.

    Args:
        precision: A float [N, 1] numpy array of precisions
        recall: A float [N, 1] numpy array of recalls

    Raises:
        ValueError: if the input is not of the correct format

    Returns:
        average_precison: The area under the precision recall curve. NaN if
            precision and recall are None.
    """
    if precision is None:
        if recall is not None:
            raise ValueError('If precision is None, recall must also be None')
        return np.NAN

    if not isinstance(precision, np.ndarray) or not isinstance(
            recall, np.ndarray):
        raise ValueError('precision and recall must be numpy array')
    if precision.dtype != np.float or recall.dtype != np.float:
        raise ValueError('input must be float numpy array.')
    if len(precision) != len(recall):
        raise ValueError('precision and recall must be of the same size.')
    if not precision.size:
        return 0.0
    if np.amin(precision) < 0 or np.amax(precision) > 1:
        raise ValueError('Precision must be in the range of [0, 1].')
    if np.amin(recall) < 0 or np.amax(recall) > 1:
        raise ValueError('recall must be in the range of [0, 1].')
    if not all(recall[i] <= recall[i + 1] for i in range(len(recall) - 1)):
        raise ValueError('recall must be a non-decreasing array')

    recall = np.concatenate([[0], recall, [1]])
    precision = np.concatenate([[0], precision, [0]])

    # Preprocess precision to be a non-decreasing array
    for i in range(len(precision) - 2, -1, -1):
        precision[i] = np.maximum(precision[i], precision[i + 1])

    indices = np.where(recall[1:] != recall[:-1])[0] + 1
    average_precision = np.sum(
        (recall[indices] - recall[indices - 1]) * precision[indices])
    return average_precision


def compute_cor_loc(num_gt_imgs_per_class,
                    num_images_correctly_detected_per_class):
    """Compute CorLoc according to the definition in the following paper.

    https://www.robots.ox.ac.uk/~vgg/rg/papers/deselaers-eccv10.pdf

    Returns nans if there are no ground truth images for a class.

    Args:
        num_gt_imgs_per_class: 1D array, representing number of images
            containing at least one object instance of a particular class
        num_images_correctly_detected_per_class: 1D array, representing number
            of images that are correctly detected at least one object instance
            of a particular class

    Returns:
        corloc_per_class: A float numpy array represents the corloc score of
            each class
    """
    # Divide by zero expected for classes with no gt examples.
    with np.errstate(divide='ignore', invalid='ignore'):
        return np.where(
            num_gt_imgs_per_class == 0, np.nan,
            num_images_correctly_detected_per_class / num_gt_imgs_per_class)
